import nltk
from nltk.stem.lancaster import LancasterStemmer
stemmer = LancasterStemmer()

import numpy as np
import tflearn
import tensorflow as tf
import random

import pickle
import json

ERROR_THRESHOLD = 0.25

def clean_up_sentence(sentence):
    # tokenize the pattern
    sentence_words = nltk.word_tokenize(sentence)
    # stem each word
    sentence_words = [stemmer.stem(word.lower()) for word in sentence_words]
    return sentence_words

# return bag of words array: 0 or 1 for each word in the bag that exists in the sentence
def bow(sentence, words, show_details=False):
    # tokenize the pattern
    sentence_words = clean_up_sentence(sentence)
    # bag of words
    bag = [0]*len(words)
    for s in sentence_words:
        for i, w in enumerate(words):
            if w == s:
                bag[i] = 1
                if show_details:
                    print("found in bag %s" % w)
    return np.array(bag)

def classify(sentence):
    # generate probabilities from the model
    results = model.predict([bow(sentence, words)])[0]
    # filter out predictions below a threshold
    results = [[i, r] for i, r in enumerate(results) if r > ERROR_THRESHOLD]
    # sort by strength of probability
    results.sort(key=lambda x: x[1], reverse=True)
    return_list = []
    for r in results:
        return_list.append((classes[r[0]], r[1]))
    # return tuple of intent and probability
    return return_list

def response(sentence, userID='123', show_details=False):
    results = classify(sentence)
    # if we have a classification then find the matching intent tag
    if results:
        # loop as long as there are matches to process
        while results:
            for i in intents['intents']:
                # find a tag matching the first results
                if i['tag'] == results[0][0]:
                    # a random response from the intent
                    return print(random.choice(i['responses']))

            results.pop(0)

# ===
data = pickle.load(open("training_data", "rb"))
words = data['words']
classes = data['classes']
train_x = data['train_x']
train_y = data['train_y']

# import our chat-bot intents file
with open('intents.json') as json_data:
    intents = json.load(json_data)

# load saved model
net = tflearn.input_data(shape=[None, len(train_x[0])])
net = tflearn.fully_connected(net, 8)
net = tflearn.fully_connected(net, 8)
net = tflearn.fully_connected(net, len(train_y[0]), activation='softmax')
net = tflearn.regression(net)
model = tflearn.DNN(net, tensorboard_dir='tflearn_logs')
model.load('./model.tflearn')

print (classify('is your shop open today?'))
print (classify('are you open today?'))
print (classify('do you take cash?'))
print (classify('what kind of mopeds do you rent?'))
print (classify('Goodbye, see you later'))
